Fetching the paper…

Reading the bibliography…

2025

From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants

Mukherjee, Manisha, Kim, Sungchul, Chen, Xiang et al.

Original
PDF
Post on X

Understand

The Adobe Experience Platform AI Assistant is a conversational tool that enables organizations to interact seamlessly with proprietary enterprise data through a chatbot.

However, due to access restrictions, Large Language Models (LLMs) cannot retrieve these internal documents, limiting their ability to generate accurate zero-shot responses.
To overcome this limitation, we use a Retrieval-Augmented Generation (RAG) framework powered by a Knowledge Graph (KG) to retrieve relevant information from external knowledge sources, enabling LLMs to answer questions over private or previously unseen document collections.
In this paper, we propose a novel approach for building a high-quality, low-noise KG.

From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants · Around

Built on

Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics . 311–318
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries. In Text summarization branches out . 74–81
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
A survey of text similarity approaches
Wael H Gomaa, Aly A Fahmy, et al · 2013
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Earlier work this paper cites.
A span-based model for joint overlapped and discontinuous named entity recognition
Original
Fei Li, ZhiChao Lin, Meishan Zhang, and Donghong Ji. 2021 · 2021
Earlier work this paper cites.
ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources
Gabriel Amaral, Odinaldo Rodrigues, and Elena Simperl. 2022 · 2022
Earlier work this paper cites.
A literature survey of recent advances in chatbots
Guendalina Caldarini, Sardar Jaf, and Kenneth McGarry. 2022 · 2022
Earlier work this paper cites.
A survey on in-context learning
Original
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Earlier work this paper cites.

Similar

Can knowledge graphs reduce hallucinations in llms?: A survey
Original
Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi, and Huan Liu. 2023 · 2023
Cited alongside, same era.
Iterative zero-shot llm prompting for knowledge graph construction
Original
Salvatore Carta, Alessandro Giuliani, Leonardo Piano, Alessandro Sebastian Podda, Livio Pompianu, and Sandro Gabriele Tiddia. 2023 · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Original
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023
Cited alongside, same era.
Stack over-flowing with results: the case for domain-specific pre-training over one-size-fits-all models
Manisha Mukherjee and Vincent J Hellendoorn. 2023 · 2023
Cited alongside, same era.
Judging llm-as-a-judge with mt-bench and chatbot arena

Then

A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods
Original
Hanlei Jin, Yang Zhang, Dan Meng, Jun Wang, and Jinghua Tan. 2024 · 2024
Later among the works it cites.
itext2kg: Incremental knowledge graphs construction using large language models. In International Conference on Web Information Systems Engineering . Springer, 214–229
Yassir Lairgi, Ludovic Moncla, Rémy Cazabet, Khalid Benabdeslem, and Pierre Cléau. 2024 · 2024
Later among the works it cites.
Contextualization distillation from large language model for knowledge graph completion
Original
Dawei Li, Zhen Tan, Tianlong Chen, and Huan Liu. 2024 · 2024
Later among the works it cites.
Unsupervised llm adaptation for question answering
Original
Kuniaki Saito, Kihyuk Sohn, Chen-Yu Lee, and Yoshitaka Ushiku. 2024 · 2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023

Cited alongside, same era.

Universalner: Targeted distillation from large language models for open named entity recognition

Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, and Hoifung Poon. 2023 · 2023

Cited alongside, same era.

From local to global: A graph rag approach to query-focused summarization

Original

Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. 2024 · 2024

Cited alongside, same era.

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models

Original

Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, and Jiawei Han. 2024 · 2024

Cited alongside, same era.

Evaluating retrieval quality in retrieval-augmented generation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2395–2400

Alireza Salemi and Hamed Zamani. 2024 · 2024

Later among the works it cites.

Knowledge graph large language model (KG-LLM) for link prediction

Dong Shu, Tianle Chen, Mingyu Jin, Chong Zhang, Mengnan Du, and Yongfeng Zhang. 2024 · 2024

Later among the works it cites.

Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation

Original

Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. 2024 · 2024

Later among the works it cites.

Adobe AI Assistant in AEP Security Fact Sheet

Adobe. 2025 · 2025

Closest in time.